IDEAS home Printed from https://ideas.repec.org/a/ags/aolpei/157527.html
   My bibliography  Save this article

A Study on Forecasting Prices of Groundnut Oil in Delhi by Arima Methodology and Artificial Neural Networks

Author

Listed:
  • Mishra, G. C.
  • Singh, A.

Abstract

Forecasting of prices of commodities specially those of agricultural commodities is very difficult because they are not only governed by demand and supply but by so many other factors which are beyond control like weather vagaries, storage capacity, transportation etc. In this paper times series namely ARIMA (Autoregressive Integrated Moving Average) methodology given by Box and Jenkins has been used for forecasting prices of edible oils and this approach has been compared with ANN (Artificial Neural Network) methodology.

Suggested Citation

  • Mishra, G. C. & Singh, A., 2013. "A Study on Forecasting Prices of Groundnut Oil in Delhi by Arima Methodology and Artificial Neural Networks," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(3), pages 1-10, September.
  • Handle: RePEc:ags:aolpei:157527
    DOI: 10.22004/ag.econ.157527
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/157527/files/agris_on-line_2013_3_mishra_singh.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.157527?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    5. Jeffrey H. Dorfman & Christopher S. McIntosh, 1990. "Results of a Price Forecasting Competition," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(3), pages 804-808.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sulaiman, H. & Malec, K. & Maitah, Mansoor, 2014. "Appropriate tools of Marketing Information System for Citrus Crop in the Lattakia Region, R. A. SYRIA," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(3), pages 1-10, September.
    2. Papagera, A. & Ioannou, K. & Zaimes, G. & Iakovoglou, V. & Simeonidou, M., 2014. "Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(4), pages 1-11, December.
    3. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    2. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    3. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    5. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    6. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    7. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    8. Cheng, Gang & Yang, Yuhong, 2015. "Forecast combination with outlier protection," International Journal of Forecasting, Elsevier, vol. 31(2), pages 223-237.
    9. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    10. Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
    11. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    12. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.
    13. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    14. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    15. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    16. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    17. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    18. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
    19. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    20. Gustavo A. Marrero, 2007. "Traditional versus unobserved components methods to forecast quarterly national account aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 129-153.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aolpei:157527. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/fevszcz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.